Image classification is a classical computer vision problem with applications to semantic image annotation, querying, and indexing. There are numerous applications that require classifiers to recognize image content, the most prominent being labeling and retrieving images semantically. Traditional training and classification procedures, for the most part, rely on two components: feature extraction and matching. Focus on either component has merited large efforts. To ensure relevant and accurate features, research has been conducted into improving the training data fidelity and segmentation truth in Torralba's LABELME®, the now-retired GOOGLE® labels, and most face/object detection/recognition training sets. Meanwhile, traditional approaches in matching/classification assume the supervised “one-versus-all” semantic labeling framework, which includes individual object detectors.
Linear programming is a mathematical method for determining a way to achieve a best outcome (such as maximum profit or lowest cost) in a given mathematical model for some list of requirements represented as linear relationships. Linear programming is a specific case of mathematical programming (mathematical optimization). More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints.
Linear programming can be applied to various fields of study. It is used in business and economics, but can also be utilized for engineering problems. Industries that use linear programming models include transportation, energy, telecommunications, and manufacturing. It has proved useful in modeling diverse types of problems in planning, routing, scheduling, assignment, and design.